Assessing the landscape of toolkits, frameworks, and authoring tools for urban visual analytics systems

Ferreira, Leonardo; Moreira, Gustavo; Hosseini, Maryam; Lage, Marcos; Ferreira, Nivan; Miranda, Fabio

doi:10.1016/j.cag.2024.104013

Computers & Graphics

2024

DOI: 10.1016/j.cag.2024.104013

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Assessing the landscape of toolkits, frameworks, and authoring tools for urban visual analytics systems

Leonardo Ferreira,

Gustavo Moreira,

Maryam Hosseini

et al.

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Cited by 3 publications

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Editorial Note Computers & Graphics Issue 123

2024

Computers & Graphics

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Editorial Note Computers & Graphics Issue 123

2024

Computers & Graphics

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Curio: A Dataflow-Based Framework for Collaborative Urban Visual Analytics

Moreira,

Hosseini,

Veiga

et al. 2025

IEEE Trans. Visual. Comput. Graphics

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Neural Network System for Predicting Anomalous Data in Applied Sensor Systems

Vladov,

Vysotska,

Sokurenko

et al. 2024

ASI

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This article advances the research on the intelligent monitoring and control of helicopter turboshaft engines in onboard conditions. The proposed neural network system for anomaly prediction functions as a module within the helicopter turboshaft engine monitoring and control expert system. A SARIMAX-based preprocessor model was developed to determine autocorrelation and partial autocorrelation in training data, accounting for dynamic changes and external factors, achieving a prediction accuracy of up to 97.9%. A modified LSTM-based predictor model with Dropout and Dense layers predicted sensor data, with a tested error margin of 0.218% for predicting the TV3-117 aircraft engine gas temperature values before the compressor turbine during one minute of helicopter flight. A reconstructor model restored missing time series values and replaced outliers with synthetic values, achieving up to 98.73% accuracy. An anomaly detector model using the concept of dissonance successfully identified two anomalies: a sensor malfunction and a sharp temperature drop within two minutes of sensor activity, with type I and II errors below 1.12 and 1.01% and a detection time under 1.611 s. The system’s AUC-ROC value of 0.818 confirms its strong ability to differentiate between normal and anomalous data, ensuring reliable and accurate anomaly detection. The limitations involve the dependency on the quality of data from onboard sensors, affected by malfunctions or noise, with the LSTM network’s accuracy (up to 97.9%) varying with helicopter conditions, and the model’s high computational demand potentially limiting real-time use in resource-constrained environments.

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Assessing the landscape of toolkits, frameworks, and authoring tools for urban visual analytics systems

Cited by 3 publications

References 122 publications

Editorial Note Computers & Graphics Issue 123

Editorial Note Computers & Graphics Issue 123

Curio: A Dataflow-Based Framework for Collaborative Urban Visual Analytics

Neural Network System for Predicting Anomalous Data in Applied Sensor Systems

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